Build production-ready ETL for RAG in 10 minutes

Learn to leverage quick, no-to-low code data pre-processing in a RAG pipeline. We demonstrate an end-to-end RAG pipeline, from ETL to model evaluation.

Watch this webinar

Speakers

Nina Lopatina

Developer Relations Engineer, Unstructured

Christopher Maddock

Head of Solutions Architecture, Unstructured

Date of recording

Wednesday, December 18

Overview

AI teams spend 80% of their time preprocessing and managing transformation pipelines. This steals precious developer resourcing from high-impact work like scaling, exploring new features, or improving accuracy.

Unstructured’s latest Platform release slashes the hours of effort to minutes, and, when paired with Ragas, allows you to evaluate the best model for your RAG pipeline in one simple workflow.

Technical Details

Watch this code-level session recording to:

Learn best practices for production-ready RAG pipelines
See Unstructured’s latest no-code, low-code, and API-based ETL for LLMs
See how easy it is to transform data in an S3 bucket to a Pinecone vector database
Learn how to use Ragas to evaluate RAG system accuracy with Unstructured + Llama 3.2
See “hours to minutes” time savings in a DAG-based workflow for RAG orchestration

Watch this code-level session recording to:

Learn best practices for production-ready RAG pipelines
See Unstructured’s latest no-code, low-code, and API-based ETL for LLMs
See how easy it is to transform data in an S3 bucket to a Pinecone vector database
Learn how to use Ragas to evaluate RAG system accuracy with Unstructured + Llama 3.2
See “hours to minutes” time savings in a DAG-based workflow for RAG orchestration

Watch this code-level session recording to:

Learn best practices for production-ready RAG pipelines
See Unstructured’s latest no-code, low-code, and API-based ETL for LLMs
See how easy it is to transform data in an S3 bucket to a Pinecone vector database
Learn how to use Ragas to evaluate RAG system accuracy with Unstructured + Llama 3.2
See “hours to minutes” time savings in a DAG-based workflow for RAG orchestration

Everyone who registers will receive a recording after the session in case you can’t join live.